How School Leadership Influences Student Learning: A Test of “The Four Paths Model”
Bibliographic record
Abstract
Purpose: This study tested a set of variables mediating school leadership’s influence on students referred to as “The four paths model.” Each path in the model includes variables with significant direct effects on student learning and which are malleable to practices included in an integrated model of effective school leadership. Research Design: Evidence for the study were responses to a survey by 1,779 teachers in 81 Texas elementary schools about the status of school leadership and all 13 variables on the four paths. Student achievement data were provided by results of state tests combining all subjects and all grades, while the count and percentage of students eligible for free or reduced-price lunch was used to estimate socioeconomic status. Confirmatory factor analysis, regression analysis, and structural equation modeling were used to analyze the data. Findings: Results uncovered a more nuanced and complex set of relationships among the four paths and their component variables than was specified in the original version of the model. School leadership significantly influenced student learning only through variables on one path, while variables on the other three paths influenced student learning only through their contribution to variables on that one path. Conclusions: Results point to the value of future research about the relationships among variables on the four paths, as well as efforts to identify latent variables among the observed variables in the study. Results of the study can be used by school leaders to more productively focus their school improvement efforts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".